Quentin CasaresData and AI leadership for regulated growth
Products and services

Portfolio value and risk review

Data & AI diligence for PE

A rapid, independent assessment of whether a portfolio company's data and AI capabilities support the investment thesis, expose hidden execution risk, or create a practical value-creation route.

A transaction- and value-creation view of data and AI that management can act on in the first 100 days.

The problem

Start with the decision failure, not the technology label.

Traditional technology diligence often inventories systems without testing whether management information can be trusted, whether key decisions depend on manual workarounds, or where AI can create value without increasing control risk.

What changes

  • A clearer view of execution risk behind the investment thesis.
  • A quantified shortlist of value-creation opportunities and dependencies.
  • An evidence-based view of data, platform, team, vendor, and AI maturity.
  • A practical first-100-days roadmap for management and the operating partner.

Scope

Concrete artefacts, not advisory fog.

The scope is deliberately explicit so the buyer knows what will exist at the end of the engagement or product implementation.

01

Management and sponsor interviews

02

Decision-grade MI and data quality assessment

03

Platform, vendor, and key-person dependency review

04

AI opportunity, governance, and third-party exposure map

05

Value-creation and risk heatmap

06

Investment committee or portfolio board readout

Method

A short sequence that ends in ownership and a decision.

Each stage has a purpose, an evidence expectation, and a defined hand-off.

  1. 01

    Thesis

    Translate the investment thesis into the decisions, data products, operational levers, and AI capabilities it depends on.

  2. 02

    Evidence

    Inspect management information, architecture, ownership, controls, delivery capacity, and vendor concentration.

  3. 03

    Value

    Identify credible revenue, margin, cash, risk, and operating-efficiency opportunities with their prerequisites.

  4. 04

    100 days

    Sequence immediate controls, management actions, and strategic investments into an executable value plan.

Strong fit

  • The investment thesis relies on better commercial or operational insight.
  • Management reporting is inconsistent, slow, or heavily manual.
  • AI is presented as upside but ownership, data, and controls are unclear.
  • The operating partner wants a repeatable review across several portfolio companies.

Boundaries

  • This is decision and operating-model diligence, not cyber penetration testing or financial audit.
  • Findings distinguish evidenced facts, management assertions, and inferences.
  • Portfolio-wide pricing is available where a common method and benchmark are useful.

Related pathways

Start at the level of commitment the problem justifies.

Products, workshops, diagnostics, and embedded leadership are designed as one commercial ladder rather than disconnected offers.

Fixed-scope diagnostic

Executive confidence review

A ten-business-day independent review of whether the organisation's data, reporting, governance, and AI controls can support the decisions now being asked of them.

View offer

Embedded executive leadership

Fractional data & AI leadership

Senior, hands-on data and AI leadership for organisations that need direction, operating discipline, and board credibility without immediately hiring a permanent executive.

View offer

Complete governance operating kit

Governance launchpad

A board-ready suite of operating templates for launching or resetting data and AI governance: charters, registers, decision rights, controls, evidence, metrics, and a 90-day mobilisation plan.

View offer

Next step

The first conversation should test fit, not manufacture urgency.

Bring the live decision, deadline, evidence gap, and stakeholder context. You will get a direct view on whether this offer is the right intervention.

Discuss a portfolio review